Paper Citation Record · LEDGER

CodeImprove: Program Adaptation for Deep Code Models

As of 23 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 1 inbound Pith citation observation for arXiv:2501.15804.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.15804 v2

Coverage vector

measured 58 of 58 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:00:50.355841Z

measured 59 of 59 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T19:18:55.873779Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-08T19:18:56.117222Z

Reference resolution

58 of 58 outbound references displayed

verified exact1
verified fuzzy19
unresolved38
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b3c6b63b-095f-4d35-984e-586086f338b9 · outbound

This paper cites On-the-fly Improving Performance of Deep Code Models via Input Denoising.

CodeImprove: Program Adaptation for Deep Code Models On-the-fly Improving Performance of Deep Code Models via Input Denoising

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:50.152067Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f847ec77-da40-4912-a941-f818d72e6142 · outbound

This paper cites Natural attack for pre-trained models of code,.

CodeImprove: Program Adaptation for Deep Code Models Natural attack for pre-trained models of code,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:50.156777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88a97641-55dc-4b89-a5a2-2ff8fc5aef88 · outbound

This paper cites Challenging Machine Learning-based Clone Detectors via Semantic-preserving Code Transformations,.

CodeImprove: Program Adaptation for Deep Code Models Challenging Machine Learning-based Clone Detectors via Semantic-preserving Code Transformations,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:50.160528Z

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Observation 3a8081dc-f3f5-4c9b-8973-985425f818cd · outbound

This paper cites CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation.

CodeImprove: Program Adaptation for Deep Code Models CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:50.164561Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1618678-1d6f-48ae-8057-1789e8bd38f7 · outbound

This paper cites Robustness, Security, Privacy, Explainability, Efficiency, and Usability of Large Language Models for Code.

CodeImprove: Program Adaptation for Deep Code Models Robustness, Security, Privacy, Explainability, Efficiency, and Usability of Large Language Models for Code

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:50.168883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 925342ba-763a-4ee9-b408-6f2efc6a042b · outbound

This paper cites Adversarial examples for models of code,.

CodeImprove: Program Adaptation for Deep Code Models Adversarial examples for models of code,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:50.961514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 71ae61eb-94e0-4622-80b6-c73522d9b55a · outbound

This paper cites Codes: towards code model generalization under distribution shift,.

CodeImprove: Program Adaptation for Deep Code Models Codes: towards code model generalization under distribution shift,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:50.950008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 523260bf-bfba-4393-89a4-34d9218b39cb · outbound

This paper cites Estimating Predictive Uncertainty Under Program Data Distribution Shift.

CodeImprove: Program Adaptation for Deep Code Models Estimating Predictive Uncertainty Under Program Data Distribution Shift

Reference 8

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a78e640-23d7-48be-9374-8f5b07d3bfb0 · outbound

This paper cites Tailoring programs for static analysis via program transformation,.

CodeImprove: Program Adaptation for Deep Code Models Tailoring programs for static analysis via program transformation,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:50.183815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff32c1e3-f570-453e-948c-6dc394a52c58 · outbound

This paper cites T-fuzz: fuzzing by program transformation,.

CodeImprove: Program Adaptation for Deep Code Models T-fuzz: fuzzing by program transformation,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:50.187375Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd72ffd9-7c6c-4e8e-ae8f-7ad41218b0e2 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,.

CodeImprove: Program Adaptation for Deep Code Models BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:50.191228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cab5bde1-5d80-466c-bd97-2ae4d112c154 · outbound

This paper cites DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter,.

CodeImprove: Program Adaptation for Deep Code Models DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:50.195024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1911433-c9b7-44d7-bd87-ca4fc205c47e · outbound

This paper cites Graphcodebert: Pre- training code representations with data flow,.

CodeImprove: Program Adaptation for Deep Code Models Graphcodebert: Pre- training code representations with data flow,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:50.198213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37359399-f5e6-4eaa-8203-77c43342500b · outbound

This paper cites Evaluating Large Language Models Trained on Code,.

CodeImprove: Program Adaptation for Deep Code Models Evaluating Large Language Models Trained on Code,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:50.201690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 234525c9-f092-4ed7-8465-e5fa45647f92 · outbound

This paper cites Data Augmentation by Program Trans- formation,.

CodeImprove: Program Adaptation for Deep Code Models Data Augmentation by Program Trans- formation,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:50.205113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6009b041-f97b-4f48-9f60-8a4c67fc2811 · outbound

This paper cites Self-checking deep neural networks in deployment,.

CodeImprove: Program Adaptation for Deep Code Models Self-checking deep neural networks in deployment,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:50.208394Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31e00f58-a238-4e21-9500-8e1ba6e35bf8 · outbound

This paper cites Repairing failure-inducing inputs with input reflection,.

CodeImprove: Program Adaptation for Deep Code Models Repairing failure-inducing inputs with input reflection,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:50.212183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ede3250-f8e5-4767-964d-3642860e579c · outbound

This paper cites On calibration of modern neural networks,.

CodeImprove: Program Adaptation for Deep Code Models On calibration of modern neural networks,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:50.215684Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc0fc42a-d553-4044-831f-e687d29abb88 · outbound

This paper cites Dissector: Input val- idation for deep learning applications by crossing-layer dissection,.

CodeImprove: Program Adaptation for Deep Code Models Dissector: Input val- idation for deep learning applications by crossing-layer dissection,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:50.219051Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a66cfb27-a6b6-436d-aab3-322d6e158a4b · outbound

This paper cites A baseline for detecting misclassified and out-of-distribution examples in neural networks,.

CodeImprove: Program Adaptation for Deep Code Models A baseline for detecting misclassified and out-of-distribution examples in neural networks,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:50.222394Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89809511-d5ae-4d10-9620-c8bcb9cb4599 · outbound

This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning,.

CodeImprove: Program Adaptation for Deep Code Models Dropout as a bayesian approximation: Representing model uncertainty in deep learning,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:50.225690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e7f6596-6399-459d-937d-5d1c2e01901b · outbound

This paper cites code2vec: Learn- ing distributed representations of code,.

CodeImprove: Program Adaptation for Deep Code Models code2vec: Learn- ing distributed representations of code,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:50.229072Z

Source-reported events for the cited work

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Observation 8ea88732-ec72-4b40-a032-009144f07808 · outbound

This paper cites Quantifying uncertainties in natural language processing tasks,.

CodeImprove: Program Adaptation for Deep Code Models Quantifying uncertainties in natural language processing tasks,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:50.232363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21967877-eaf7-4533-9f57-4c937c2fcef6 · outbound

This paper cites Towards better confidence estimation for neural models,.

CodeImprove: Program Adaptation for Deep Code Models Towards better confidence estimation for neural models,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:50.235789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18cfd1b1-c694-4f8d-8b6b-465c50aed925 · outbound

This paper cites Addressing failure prediction by learning model confidence,.

CodeImprove: Program Adaptation for Deep Code Models Addressing failure prediction by learning model confidence,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:50.239349Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11b899e1-2e06-4605-9519-0e84ed397098 · outbound

This paper cites an unresolved cited work.

CodeImprove: Program Adaptation for Deep Code Models Unresolved cited work

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:50.242750Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd45211e-e819-4f21-9862-81a992b68bd4 · outbound

This paper cites Unsupervised risk estimation using only conditional independence structure,.

CodeImprove: Program Adaptation for Deep Code Models Unsupervised risk estimation using only conditional independence structure,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:50.246285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6bc155be-8f02-4b9a-b84a-0309bbd006a9 · outbound

This paper cites A mathematical theory of communication,.

CodeImprove: Program Adaptation for Deep Code Models A mathematical theory of communication,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:50.249956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c9bf45f-223c-4c38-8606-0d9d7344c8cd · outbound

This paper cites Codeimprove repository.

CodeImprove: Program Adaptation for Deep Code Models Codeimprove repository

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:50.813237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 37b46f08-d132-46b0-8e48-a78c8aec3e87 · outbound

This paper cites Towards evaluating the robustness of neural networks,.

CodeImprove: Program Adaptation for Deep Code Models Towards evaluating the robustness of neural networks,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:50.257437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd0402a1-7e11-4f7b-a23c-2dd08f436c2b · outbound

This paper cites Distillation as a defense to adversarial perturbations against deep neural networks,.

CodeImprove: Program Adaptation for Deep Code Models Distillation as a defense to adversarial perturbations against deep neural networks,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:50.796033Z

Source-reported events for the cited work

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Observation c3f9c0f9-f7c8-4bd7-9055-e0fa5af201b4 · outbound

This paper cites Intriguing properties of neural networks.

CodeImprove: Program Adaptation for Deep Code Models Intriguing properties of neural networks

Reference 32

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Source-reported events for the cited work

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Observation c6e15abd-42ad-492d-8282-248ac4f714b7 · outbound

This paper cites On the Effectiveness of Defensive Distillation.

CodeImprove: Program Adaptation for Deep Code Models On the Effectiveness of Defensive Distillation

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:00:50.410524Z

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Observation e3143add-3c03-4f24-9d9e-a1b165e4019e · outbound

This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles,.

CodeImprove: Program Adaptation for Deep Code Models Simple and scalable predictive uncertainty estimation using deep ensembles,

Reference 34

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Source-reported events for the cited work

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Observation d49ee672-3ad7-4bb2-abc4-f32fbaf39ed8 · outbound

This paper cites Random search algorithms,.

CodeImprove: Program Adaptation for Deep Code Models Random search algorithms,

Reference 35

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Source-reported events for the cited work

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Observation 703eaf92-854e-4d1b-abb9-6f024740e34f · outbound

This paper cites Hill-climbing search,.

CodeImprove: Program Adaptation for Deep Code Models Hill-climbing search,

Reference 36

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Observation b013fb97-409b-4a21-826c-e83123f207d2 · outbound

This paper cites Search-based software engineering,.

CodeImprove: Program Adaptation for Deep Code Models Search-based software engineering,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:50.762311Z

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Observation d0aa7cf9-e63d-43a2-9ea1-78a8fe12dff2 · outbound

This paper cites Bfgs optimization for faster and automated supervised learning,.

CodeImprove: Program Adaptation for Deep Code Models Bfgs optimization for faster and automated supervised learning,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:50.751068Z

Source-reported events for the cited work

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Observation 425669da-7bd6-4977-a38b-0ce9b7466efe · outbound

This paper cites The relationship between precision-recall and roc curves,.

CodeImprove: Program Adaptation for Deep Code Models The relationship between precision-recall and roc curves,

Reference 39

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Observation 5f752f21-6eb0-4b56-b340-5a5a053f5f80 · outbound

This paper cites A survey of uncertainty in deep neural networks,.

CodeImprove: Program Adaptation for Deep Code Models A survey of uncertainty in deep neural networks,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:50.732816Z

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Observation e72f50e3-f8d1-4d68-a547-a42738e35686 · outbound

This paper cites Unveiling code pre-trained models: Investigating syntax and semantics capacities,.

CodeImprove: Program Adaptation for Deep Code Models Unveiling code pre-trained models: Investigating syntax and semantics capacities,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:50.720940Z

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Observation 4724fb29-5984-47c5-9467-1b8e060ea10c · outbound

This paper cites Densely connected convolutional networks,.

CodeImprove: Program Adaptation for Deep Code Models Densely connected convolutional networks,

Reference 42

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Observation 5aae42c6-2870-4b1a-a244-f2fd55dc760d · outbound

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CodeImprove: Program Adaptation for Deep Code Models Deep residual learning for image recognition,

Reference 43

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Observation 33844fc2-6b71-477c-b52f-c5d6291f4636 · outbound

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CodeImprove: Program Adaptation for Deep Code Models Codeimprove

Reference 44

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Observation 75b27a77-58dc-4404-b57e-a482a4649aa1 · outbound

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CodeImprove: Program Adaptation for Deep Code Models Genetic algorithm: Review and application,

Reference 45

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Observation bef80e8a-7d35-428d-b00e-81c73a403c61 · outbound

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CodeImprove: Program Adaptation for Deep Code Models Genetic algorithms,

Reference 46

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Observation 98346d9e-8269-4057-bb1e-5a1e7e6c235a · outbound

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CodeImprove: Program Adaptation for Deep Code Models A genetic algorithm tutorial,

Reference 47

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Observation fe40489a-5ead-48d6-8ad0-fd18eb6ea9be · outbound

This paper cites Devign: Effective vul- nerability identification by learning comprehensive program semantics via graph neural networks,.

CodeImprove: Program Adaptation for Deep Code Models Devign: Effective vul- nerability identification by learning comprehensive program semantics via graph neural networks,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:50.647156Z

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Observation 50a17a54-944f-4211-8125-43b767578f76 · outbound

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CodeImprove: Program Adaptation for Deep Code Models Convolutional neural networks on assembly code for predicting software defects,

Reference 49

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Observation c74c2b34-4f90-43bd-96d3-fe4c081c11e6 · outbound

This paper cites CodeBERT: A Pre-Trained Model for Programming and Natural Languages,.

CodeImprove: Program Adaptation for Deep Code Models CodeBERT: A Pre-Trained Model for Programming and Natural Languages,

Reference 50

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CodeImprove: Program Adaptation for Deep Code Models RoBERTa: A Robustly Optimized BERT Pretraining Approach,

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:50.331383Z

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Observation bbf07bc0-2dee-4247-8c77-0a86ee66f2bc · outbound

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CodeImprove: Program Adaptation for Deep Code Models Code difference guided adversarial example generation for deep code models,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:50.615031Z

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Observation 26df8ca8-1851-444b-9abb-ab381d14cf6c · outbound

This paper cites Natural attack for pre-trained models of code,.

CodeImprove: Program Adaptation for Deep Code Models Natural attack for pre-trained models of code,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:50.603663Z

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Observation 0e8d2d9f-1a38-49e5-90e1-0c668ce3eb5c · outbound

This paper cites Towards robustness of deep program processing models—detection, estimation, and enhancement,.

CodeImprove: Program Adaptation for Deep Code Models Towards robustness of deep program processing models—detection, estimation, and enhancement,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:50.341452Z

Source-reported events for the cited work

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Observation a857601e-7479-4d96-ab96-980a1d8323dd · outbound

This paper cites A twofold siamese network for real-time object tracking,.

CodeImprove: Program Adaptation for Deep Code Models A twofold siamese network for real-time object tracking,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:50.591097Z

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Observation f85efb64-7bc0-4d4f-a79b-5c9383b33758 · outbound

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CodeImprove: Program Adaptation for Deep Code Models Variable kernel density estimation,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:50.580203Z

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Observation f933d134-1063-49ba-b9b0-d11bdc3c2b2f · outbound

This paper cites Generating adversarial examples for holding robustness of source code processing models,.

CodeImprove: Program Adaptation for Deep Code Models Generating adversarial examples for holding robustness of source code processing models,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:50.568597Z

Source-reported events for the cited work

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Observation 62190896-04ce-4c8b-b3ce-896e77122e94 · outbound

This paper cites Generating Adversarial Computer Programs using Optimized Obfuscations.

CodeImprove: Program Adaptation for Deep Code Models Generating Adversarial Computer Programs using Optimized Obfuscations

Reference 58

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Pith citing papers

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Framework for On the Fly Input Refinement for Deep Learning Models cites this paper.

Framework for On the Fly Input Refinement for Deep Learning Models CodeImprove: Program Adaptation for Deep Code Models

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Resolution: verified exact; local_arxiv, observed 2026-08-08T19:18:56.122525Z

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